/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* Experiment.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.core.AdditionalMeasureProducer;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.converters.AbstractFileLoader;
import weka.core.converters.ConverterUtils;
import weka.core.xml.KOML;
import weka.core.xml.XMLOptions;
import weka.experiment.xml.XMLExperiment;
import java.beans.PropertyDescriptor;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import java.lang.reflect.Array;
import java.lang.reflect.Method;
import java.util.Enumeration;
import java.util.Vector;
import javax.swing.DefaultListModel;
/**
* Holds all the necessary configuration information for a standard
* type experiment. This object is able to be serialized for storage
* on disk.
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -L <num>
* The lower run number to start the experiment from.
* (default 1)</pre>
*
* <pre> -U <num>
* The upper run number to end the experiment at (inclusive).
* (default 10)</pre>
*
* <pre> -T <arff file>
* The dataset to run the experiment on.
* (required, may be specified multiple times)</pre>
*
* <pre> -P <class name>
* The full class name of a ResultProducer (required).
* eg: weka.experiment.RandomSplitResultProducer</pre>
*
* <pre> -D <class name>
* The full class name of a ResultListener (required).
* eg: weka.experiment.CSVResultListener</pre>
*
* <pre> -N <string>
* A string containing any notes about the experiment.
* (default none)</pre>
*
* <pre>
* Options specific to result producer weka.experiment.RandomSplitResultProducer:
* </pre>
*
* <pre> -P <percent>
* The percentage of instances to use for training.
* (default 66)</pre>
*
* <pre> -D
* Save raw split evaluator output.</pre>
*
* <pre> -O <file/directory name/path>
* The filename where raw output will be stored.
* If a directory name is specified then then individual
* outputs will be gzipped, otherwise all output will be
* zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)</pre>
*
* <pre> -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator</pre>
*
* <pre> -R
* Set when data is not to be randomized and the data sets' size.
* Is not to be determined via probabilistic rounding.</pre>
*
* <pre>
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
* </pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options after -- will be passed to the result producer. <p>
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 6385 $
*/
public class Experiment
implements Serializable, OptionHandler, RevisionHandler {
/** for serialization */
static final long serialVersionUID = 44945596742646663L;
/** The filename extension that should be used for experiment files */
public static String FILE_EXTENSION = ".exp";
/** Where results will be sent */
protected ResultListener m_ResultListener = new InstancesResultListener();
/** The result producer */
protected ResultProducer m_ResultProducer = new RandomSplitResultProducer();
/** Lower run number */
protected int m_RunLower = 1;
/** Upper run number */
protected int m_RunUpper = 10;
/** An array of dataset files */
protected DefaultListModel m_Datasets = new DefaultListModel();
/** True if the exp should also iterate over a property of the RP */
protected boolean m_UsePropertyIterator = false;
/** The path to the iterator property */
protected PropertyNode [] m_PropertyPath;
/** The array of values to set the property to */
protected Object m_PropertyArray;
/** User notes about the experiment */
protected String m_Notes = "";
/** Method names of additional measures of objects contained in the
custom property iterator. Only methods names beginning with "measure"
and returning doubles are recognised */
protected String [] m_AdditionalMeasures = null;
/** True if the class attribute is the first attribute for all
datasets involved in this experiment. */
protected boolean m_ClassFirst = false;
/** If true an experiment will advance the current data set befor
any custom itererator */
protected boolean m_AdvanceDataSetFirst = true;
/**
* Sets whether the first attribute is treated as the class
* for all datasets involved in the experiment. This information
* is not output with the result of the experiments!
*
* @param flag whether the class attribute is the first and not the last
*/
public void classFirst(boolean flag) {
m_ClassFirst = flag;
}
/**
* Get the value of m_DataSetFirstFirst.
*
* @return Value of m_DataSetFirstFirst.
*/
public boolean getAdvanceDataSetFirst() {
return m_AdvanceDataSetFirst;
}
/**
* Set the value of m_AdvanceDataSetFirst.
*
* @param newAdvanceDataSetFirst Value to assign to m_AdvanceRunFirst.
*/
public void setAdvanceDataSetFirst(boolean newAdvanceDataSetFirst) {
m_AdvanceDataSetFirst = newAdvanceDataSetFirst;
}
/**
* Gets whether the custom property iterator should be used.
*
* @return true if so
*/
public boolean getUsePropertyIterator() {
return m_UsePropertyIterator;
}
/**
* Sets whether the custom property iterator should be used.
*
* @param newUsePropertyIterator true if so
*/
public void setUsePropertyIterator(boolean newUsePropertyIterator) {
m_UsePropertyIterator = newUsePropertyIterator;
}
/**
* Gets the path of properties taken to get to the custom property
* to iterate over.
*
* @return an array of PropertyNodes
*/
public PropertyNode [] getPropertyPath() {
return m_PropertyPath;
}
/**
* Sets the path of properties taken to get to the custom property
* to iterate over.
*
* @param newPropertyPath an array of PropertyNodes
*/
public void setPropertyPath(PropertyNode [] newPropertyPath) {
m_PropertyPath = newPropertyPath;
}
/**
* Sets the array of values to set the custom property to.
*
* @param newPropArray a value of type Object which should be an
* array of the appropriate values.
*/
public void setPropertyArray(Object newPropArray) {
m_PropertyArray = newPropArray;
}
/**
* Gets the array of values to set the custom property to.
*
* @return a value of type Object which should be an
* array of the appropriate values.
*/
public Object getPropertyArray() {
return m_PropertyArray;
}
/**
* Gets the number of custom iterator values that have been defined
* for the experiment.
*
* @return the number of custom property iterator values.
*/
public int getPropertyArrayLength() {
return Array.getLength(m_PropertyArray);
}
/**
* Gets a specified value from the custom property iterator array.
*
* @param index the index of the value wanted
* @return the property array value
*/
public Object getPropertyArrayValue(int index) {
return Array.get(m_PropertyArray, index);
}
/* These may potentially want to be made un-transient if it is decided
* that experiments may be saved mid-run and later resumed
*/
/** The current run number when the experiment is running */
protected transient int m_RunNumber;
/** The current dataset number when the experiment is running */
protected transient int m_DatasetNumber;
/** The current custom property value index when the experiment is running */
protected transient int m_PropertyNumber;
/** True if the experiment has finished running */
protected transient boolean m_Finished = true;
/** The dataset currently being used */
protected transient Instances m_CurrentInstances;
/** The custom property value that has actually been set */
protected transient int m_CurrentProperty;
/**
* When an experiment is running, this returns the current run number.
*
* @return the current run number.
*/
public int getCurrentRunNumber() {
return m_RunNumber;
}
/**
* When an experiment is running, this returns the current dataset number.
*
* @return the current dataset number.
*/
public int getCurrentDatasetNumber() {
return m_DatasetNumber;
}
/**
* When an experiment is running, this returns the index of the
* current custom property value.
*
* @return the index of the current custom property value.
*/
public int getCurrentPropertyNumber() {
return m_PropertyNumber;
}
/**
* Prepares an experiment for running, initializing current iterator
* settings.
*
* @throws Exception if an error occurs
*/
public void initialize() throws Exception {
m_RunNumber = getRunLower();
m_DatasetNumber = 0;
m_PropertyNumber = 0;
m_CurrentProperty = -1;
m_CurrentInstances = null;
m_Finished = false;
if (m_UsePropertyIterator && (m_PropertyArray == null)) {
throw new Exception("Null array for property iterator");
}
if (getRunLower() > getRunUpper()) {
throw new Exception("Lower run number is greater than upper run number");
}
if (getDatasets().size() == 0) {
throw new Exception("No datasets have been specified");
}
if (m_ResultProducer == null) {
throw new Exception("No ResultProducer set");
}
if (m_ResultListener == null) {
throw new Exception("No ResultListener set");
}
// if (m_UsePropertyIterator && (m_PropertyArray != null)) {
determineAdditionalResultMeasures();
// }
m_ResultProducer.setResultListener(m_ResultListener);
m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures);
m_ResultProducer.preProcess();
// constrain the additional measures to be only those allowable
// by the ResultListener
String [] columnConstraints = m_ResultListener.
determineColumnConstraints(m_ResultProducer);
if (columnConstraints != null) {
m_ResultProducer.setAdditionalMeasures(columnConstraints);
}
}
/**
* Iterate over the objects in the property array to determine what
* (if any) additional measures they support
*
* @throws Exception if additional measures don't comply to the naming
* convention (starting with "measure")
*/
private void determineAdditionalResultMeasures() throws Exception {
m_AdditionalMeasures = null;
FastVector measureNames = new FastVector();
// first try the result producer, then property array if applicable
if (m_ResultProducer instanceof AdditionalMeasureProducer) {
Enumeration am = ((AdditionalMeasureProducer)m_ResultProducer).
enumerateMeasures();
while (am.hasMoreElements()) {
String mname = (String)am.nextElement();
if (mname.startsWith("measure")) {
if (measureNames.indexOf(mname) == -1) {
measureNames.addElement(mname);
}
} else {
throw new Exception ("Additional measures in "
+ m_ResultProducer.getClass().getName()
+" must obey the naming convention"
+" of starting with \"measure\"");
}
}
}
if (m_UsePropertyIterator && (m_PropertyArray != null)) {
for (int i = 0; i < Array.getLength(m_PropertyArray); i++) {
Object current = Array.get(m_PropertyArray, i);
if (current instanceof AdditionalMeasureProducer) {
Enumeration am = ((AdditionalMeasureProducer)current).
enumerateMeasures();
while (am.hasMoreElements()) {
String mname = (String)am.nextElement();
if (mname.startsWith("measure")) {
if (measureNames.indexOf(mname) == -1) {
measureNames.addElement(mname);
}
} else {
throw new Exception ("Additional measures in "
+ current.getClass().getName()
+" must obey the naming convention"
+" of starting with \"measure\"");
}
}
}
}
}
if (measureNames.size() > 0) {
m_AdditionalMeasures = new String [measureNames.size()];
for (int i=0;i<measureNames.size();i++) {
m_AdditionalMeasures[i] = (String)measureNames.elementAt(i);
}
}
}
/**
* Recursively sets the custom property value, by setting all values
* along the property path.
*
* @param propertyDepth the current position along the property path
* @param origValue the value to set the property to
* @throws Exception if an error occurs
*/
protected void setProperty(int propertyDepth, Object origValue)
throws Exception {
PropertyDescriptor current = m_PropertyPath[propertyDepth].property;
Object subVal = null;
if (propertyDepth < m_PropertyPath.length - 1) {
Method getter = current.getReadMethod();
Object getArgs [] = { };
subVal = getter.invoke(origValue, getArgs);
setProperty(propertyDepth + 1, subVal);
} else {
subVal = Array.get(m_PropertyArray, m_PropertyNumber);
}
Method setter = current.getWriteMethod();
Object [] args = { subVal };
setter.invoke(origValue, args);
}
/**
* Returns true if there are more iterations to carry out in the experiment.
*
* @return true if so
*/
public boolean hasMoreIterations() {
return !m_Finished;
}
/**
* Carries out the next iteration of the experiment.
*
* @throws Exception if an error occurs
*/
public void nextIteration() throws Exception {
if (m_UsePropertyIterator) {
if (m_CurrentProperty != m_PropertyNumber) {
setProperty(0, m_ResultProducer);
m_CurrentProperty = m_PropertyNumber;
}
}
if (m_CurrentInstances == null) {
File currentFile = (File) getDatasets().elementAt(m_DatasetNumber);
AbstractFileLoader loader = ConverterUtils.getLoaderForFile(currentFile);
loader.setFile(currentFile);
Instances data = new Instances(loader.getDataSet());
// only set class attribute if not already done by loader
if (data.classIndex() == -1) {
if (m_ClassFirst) {
data.setClassIndex(0);
} else {
data.setClassIndex(data.numAttributes() - 1);
}
}
m_CurrentInstances = data;
m_ResultProducer.setInstances(m_CurrentInstances);
}
m_ResultProducer.doRun(m_RunNumber);
advanceCounters();
}
/**
* Increments iteration counters appropriately.
*/
public void advanceCounters() {
if (m_AdvanceDataSetFirst) {
m_RunNumber ++;
if (m_RunNumber > getRunUpper()) {
m_RunNumber = getRunLower();
m_DatasetNumber ++;
m_CurrentInstances = null;
if (m_DatasetNumber >= getDatasets().size()) {
m_DatasetNumber = 0;
if (m_UsePropertyIterator) {
m_PropertyNumber ++;
if (m_PropertyNumber >= Array.getLength(m_PropertyArray)) {
m_Finished = true;
}
} else {
m_Finished = true;
}
}
}
} else { // advance by custom iterator before data set
m_RunNumber ++;
if (m_RunNumber > getRunUpper()) {
m_RunNumber = getRunLower();
if (m_UsePropertyIterator) {
m_PropertyNumber ++;
if (m_PropertyNumber >= Array.getLength(m_PropertyArray)) {
m_PropertyNumber = 0;
m_DatasetNumber ++;
m_CurrentInstances = null;
if (m_DatasetNumber >= getDatasets().size()) {
m_Finished = true;
}
}
} else {
m_DatasetNumber ++;
m_CurrentInstances = null;
if (m_DatasetNumber >= getDatasets().size()) {
m_Finished = true;
}
}
}
}
}
public void runExperiment(boolean verbose) {
while (hasMoreIterations()) {
try {
if (verbose) {
String current = "Iteration:";
if (getUsePropertyIterator()) {
int cnum = getCurrentPropertyNumber();
String ctype = getPropertyArray().getClass().getComponentType().getName();
int lastDot = ctype.lastIndexOf('.');
if (lastDot != -1) {
ctype = ctype.substring(lastDot + 1);
}
String cname = " " + ctype + "="
+ (cnum + 1) + ":"
+ getPropertyArrayValue(cnum).getClass().getName();
current += cname;
}
String dname = ((File) getDatasets()
.elementAt(getCurrentDatasetNumber()))
.getName();
current += " Dataset=" + dname
+ " Run=" + (getCurrentRunNumber());
System.out.println(current);
}
nextIteration();
} catch (Exception ex) {
ex.printStackTrace();
System.err.println(ex.getMessage());
advanceCounters(); // Try to keep plowing through
}
}
}
/**
* Runs all iterations of the experiment, continuing past errors.
*/
public void runExperiment() {
runExperiment(false);
}
/**
* Signals that the experiment is finished running, so that cleanup
* can be done.
*
* @throws Exception if an error occurs
*/
public void postProcess() throws Exception {
m_ResultProducer.postProcess();
}
/**
* Gets the datasets in the experiment.
*
* @return the datasets in the experiment.
*/
public DefaultListModel getDatasets() {
return m_Datasets;
}
/**
* Set the datasets to use in the experiment
* @param ds the list of datasets to use
*/
public void setDatasets(DefaultListModel ds) {
m_Datasets = ds;
}
/**
* Gets the result listener where results will be sent.
*
* @return the result listener where results will be sent.
*/
public ResultListener getResultListener() {
return m_ResultListener;
}
/**
* Sets the result listener where results will be sent.
*
* @param newResultListener the result listener where results will be sent.
*/
public void setResultListener(ResultListener newResultListener) {
m_ResultListener = newResultListener;
}
/**
* Get the result producer used for the current experiment.
*
* @return the result producer used for the current experiment.
*/
public ResultProducer getResultProducer() {
return m_ResultProducer;
}
/**
* Set the result producer used for the current experiment.
*
* @param newResultProducer result producer to use for the current
* experiment.
*/
public void setResultProducer(ResultProducer newResultProducer) {
m_ResultProducer = newResultProducer;
}
/**
* Get the upper run number for the experiment.
*
* @return the upper run number for the experiment.
*/
public int getRunUpper() {
return m_RunUpper;
}
/**
* Set the upper run number for the experiment.
*
* @param newRunUpper the upper run number for the experiment.
*/
public void setRunUpper(int newRunUpper) {
m_RunUpper = newRunUpper;
}
/**
* Get the lower run number for the experiment.
*
* @return the lower run number for the experiment.
*/
public int getRunLower() {
return m_RunLower;
}
/**
* Set the lower run number for the experiment.
*
* @param newRunLower the lower run number for the experiment.
*/
public void setRunLower(int newRunLower) {
m_RunLower = newRunLower;
}
/**
* Get the user notes.
*
* @return User notes associated with the experiment.
*/
public String getNotes() {
return m_Notes;
}
/**
* Set the user notes.
*
* @param newNotes New user notes.
*/
public void setNotes(String newNotes) {
m_Notes = newNotes;
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(6);
newVector.addElement(new Option(
"\tThe lower run number to start the experiment from.\n"
+"\t(default 1)",
"L", 1,
"-L <num>"));
newVector.addElement(new Option(
"\tThe upper run number to end the experiment at (inclusive).\n"
+"\t(default 10)",
"U", 1,
"-U <num>"));
newVector.addElement(new Option(
"\tThe dataset to run the experiment on.\n"
+ "\t(required, may be specified multiple times)",
"T", 1,
"-T <arff file>"));
newVector.addElement(new Option(
"\tThe full class name of a ResultProducer (required).\n"
+"\teg: weka.experiment.RandomSplitResultProducer",
"P", 1,
"-P <class name>"));
newVector.addElement(new Option(
"\tThe full class name of a ResultListener (required).\n"
+"\teg: weka.experiment.CSVResultListener",
"D", 1,
"-D <class name>"));
newVector.addElement(new Option(
"\tA string containing any notes about the experiment.\n"
+"\t(default none)",
"N", 1,
"-N <string>"));
if ((m_ResultProducer != null) &&
(m_ResultProducer instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to result producer "
+ m_ResultProducer.getClass().getName() + ":"));
Enumeration enm = ((OptionHandler)m_ResultProducer).listOptions();
while (enm.hasMoreElements()) {
newVector.addElement(enm.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -L <num>
* The lower run number to start the experiment from.
* (default 1)</pre>
*
* <pre> -U <num>
* The upper run number to end the experiment at (inclusive).
* (default 10)</pre>
*
* <pre> -T <arff file>
* The dataset to run the experiment on.
* (required, may be specified multiple times)</pre>
*
* <pre> -P <class name>
* The full class name of a ResultProducer (required).
* eg: weka.experiment.RandomSplitResultProducer</pre>
*
* <pre> -D <class name>
* The full class name of a ResultListener (required).
* eg: weka.experiment.CSVResultListener</pre>
*
* <pre> -N <string>
* A string containing any notes about the experiment.
* (default none)</pre>
*
* <pre>
* Options specific to result producer weka.experiment.RandomSplitResultProducer:
* </pre>
*
* <pre> -P <percent>
* The percentage of instances to use for training.
* (default 66)</pre>
*
* <pre> -D
* Save raw split evaluator output.</pre>
*
* <pre> -O <file/directory name/path>
* The filename where raw output will be stored.
* If a directory name is specified then then individual
* outputs will be gzipped, otherwise all output will be
* zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)</pre>
*
* <pre> -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator</pre>
*
* <pre> -R
* Set when data is not to be randomized and the data sets' size.
* Is not to be determined via probabilistic rounding.</pre>
*
* <pre>
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
* </pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options after -- will be passed to the result producer. <p>
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String [] options) throws Exception {
String lowerString = Utils.getOption('L', options);
if (lowerString.length() != 0) {
setRunLower(Integer.parseInt(lowerString));
} else {
setRunLower(1);
}
String upperString = Utils.getOption('U', options);
if (upperString.length() != 0) {
setRunUpper(Integer.parseInt(upperString));
} else {
setRunUpper(10);
}
if (getRunLower() > getRunUpper()) {
throw new Exception("Lower (" + getRunLower()
+ ") is greater than upper ("
+ getRunUpper() + ")");
}
setNotes(Utils.getOption('N', options));
getDatasets().removeAllElements();
String dataName;
do {
dataName = Utils.getOption('T', options);
if (dataName.length() != 0) {
File dataset = new File(dataName);
getDatasets().addElement(dataset);
}
} while (dataName.length() != 0);
if (getDatasets().size() == 0) {
throw new Exception("Required: -T <arff file name>");
}
String rlName = Utils.getOption('D', options);
if (rlName.length() == 0) {
throw new Exception("Required: -D <ResultListener class name>");
}
rlName = rlName.trim();
// split off any options
int breakLoc = rlName.indexOf(' ');
String clName = rlName;
String rlOptionsString = "";
String [] rlOptions = null;
if (breakLoc != -1) {
clName = rlName.substring(0, breakLoc);
rlOptionsString = rlName.substring(breakLoc).trim();
rlOptions = Utils.splitOptions(rlOptionsString);
}
setResultListener((ResultListener)Utils.forName(ResultListener.class,
clName, rlOptions));
String rpName = Utils.getOption('P', options);
if (rpName.length() == 0) {
throw new Exception("Required: -P <ResultProducer class name>");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// RP.
//GHF -- nice idea, but it prevents you from using result producers that
// have *required* parameters
setResultProducer((ResultProducer)Utils.forName(
ResultProducer.class,
rpName,
Utils.partitionOptions(options) )); //GHF
//GHF if (getResultProducer() instanceof OptionHandler) {
//GHF ((OptionHandler) getResultProducer())
//GHF .setOptions(Utils.partitionOptions(options));
//GHF }
}
/**
* Gets the current settings of the experiment iterator.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
// Currently no way to set custompropertyiterators from the command line
m_UsePropertyIterator = false;
m_PropertyPath = null;
m_PropertyArray = null;
String [] rpOptions = new String [0];
if ((m_ResultProducer != null) &&
(m_ResultProducer instanceof OptionHandler)) {
rpOptions = ((OptionHandler)m_ResultProducer).getOptions();
}
String [] options = new String [rpOptions.length
+ getDatasets().size() * 2
+ 11];
int current = 0;
options[current++] = "-L"; options[current++] = "" + getRunLower();
options[current++] = "-U"; options[current++] = "" + getRunUpper();
if (getDatasets().size() != 0) {
for (int i = 0; i < getDatasets().size(); i++) {
options[current++] = "-T";
options[current++] = getDatasets().elementAt(i).toString();
}
}
if (getResultListener() != null) {
options[current++] = "-D";
options[current++] = getResultListener().getClass().getName();
}
if (getResultProducer() != null) {
options[current++] = "-P";
options[current++] = getResultProducer().getClass().getName();
}
if (!getNotes().equals("")) {
options[current++] = "-N"; options[current++] = getNotes();
}
options[current++] = "--";
System.arraycopy(rpOptions, 0, options, current,
rpOptions.length);
current += rpOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Gets a string representation of the experiment configuration.
*
* @return a value of type 'String'
*/
public String toString() {
String result = "Runs from: " + m_RunLower + " to: " + m_RunUpper + '\n';
result += "Datasets:";
for (int i = 0; i < m_Datasets.size(); i ++) {
result += " " + m_Datasets.elementAt(i);
}
result += '\n';
result += "Custom property iterator: "
+ (m_UsePropertyIterator ? "on" : "off")
+ "\n";
if (m_UsePropertyIterator) {
if (m_PropertyPath == null) {
throw new Error("*** null propertyPath ***");
}
if (m_PropertyArray == null) {
throw new Error("*** null propertyArray ***");
}
if (m_PropertyPath.length > 1) {
result += "Custom property path:\n";
for (int i = 0; i < m_PropertyPath.length - 1; i++) {
PropertyNode pn = m_PropertyPath[i];
result += "" + (i + 1) + " " + pn.parentClass.getName()
+ "::" + pn.toString()
+ ' ' + pn.value.toString() + '\n';
}
}
result += "Custom property name:"
+ m_PropertyPath[m_PropertyPath.length - 1].toString() + '\n';
result += "Custom property values:\n";
for (int i = 0; i < Array.getLength(m_PropertyArray); i++) {
Object current = Array.get(m_PropertyArray, i);
result += " " + (i + 1)
+ " " + current.getClass().getName()
+ " " + current.toString() + '\n';
}
}
result += "ResultProducer: " + m_ResultProducer + '\n';
result += "ResultListener: " + m_ResultListener + '\n';
if (!getNotes().equals("")) {
result += "Notes: " + getNotes();
}
return result;
}
/**
* Loads an experiment from a file.
*
* @param filename the file to load the experiment from
* @return the experiment
* @throws Exception if loading fails
*/
public static Experiment read(String filename) throws Exception {
Experiment result;
// KOML?
if ( (KOML.isPresent()) && (filename.toLowerCase().endsWith(KOML.FILE_EXTENSION)) ) {
result = (Experiment) KOML.read(filename);
}
// XML?
else if (filename.toLowerCase().endsWith(".xml")) {
XMLExperiment xml = new XMLExperiment();
result = (Experiment) xml.read(filename);
}
// binary
else {
FileInputStream fi = new FileInputStream(filename);
ObjectInputStream oi = new ObjectInputStream(
new BufferedInputStream(fi));
result = (Experiment)oi.readObject();
oi.close();
}
return result;
}
/**
* Writes the experiment to disk.
*
* @param filename the file to write to
* @param exp the experiment to save
* @throws Exception if writing fails
*/
public static void write(String filename, Experiment exp) throws Exception {
// KOML?
if ( (KOML.isPresent()) && (filename.toLowerCase().endsWith(KOML.FILE_EXTENSION)) ) {
KOML.write(filename, exp);
}
// XML?
else if (filename.toLowerCase().endsWith(".xml")) {
XMLExperiment xml = new XMLExperiment();
xml.write(filename, exp);
}
// binary
else {
FileOutputStream fo = new FileOutputStream(filename);
ObjectOutputStream oo = new ObjectOutputStream(
new BufferedOutputStream(fo));
oo.writeObject(exp);
oo.close();
}
}
/**
* Configures/Runs the Experiment from the command line.
*
* @param args command line arguments to the Experiment.
*/
public static void main(String[] args) {
try {
Experiment exp = null;
// get options from XML?
String xmlOption = Utils.getOption("xml", args);
if (!xmlOption.equals(""))
args = new XMLOptions(xmlOption).toArray();
String expFile = Utils.getOption('l', args);
String saveFile = Utils.getOption('s', args);
boolean runExp = Utils.getFlag('r', args);
boolean verbose = Utils.getFlag("verbose", args);
if (expFile.length() == 0) {
exp = new Experiment();
try {
exp.setOptions(args);
Utils.checkForRemainingOptions(args);
} catch (Exception ex) {
ex.printStackTrace();
String result = "Usage:\n\n"
+ "-l <exp|xml file>\n"
+ "\tLoad experiment from file (default use cli options).\n"
+ "\tThe type is determined, based on the extension ("
+ FILE_EXTENSION + " or .xml)\n"
+ "-s <exp|xml file>\n"
+ "\tSave experiment to file after setting other options.\n"
+ "\tThe type is determined, based on the extension ("
+ FILE_EXTENSION + " or .xml)\n"
+ "\t(default don't save)\n"
+ "-r\n"
+ "\tRun experiment (default don't run)\n"
+ "-xml <filename | xml-string>\n"
+ "\tget options from XML-Data instead from parameters.\n"
+ "-verbose\n"
+ "\toutput progress information to std out."
+ "\n";
Enumeration enm = ((OptionHandler)exp).listOptions();
while (enm.hasMoreElements()) {
Option option = (Option) enm.nextElement();
result += option.synopsis() + "\n";
result += option.description() + "\n";
}
throw new Exception(result + "\n" + ex.getMessage());
}
} else {
exp = read(expFile);
// allow extra datasets to be added to pre-loaded experiment from command line
String dataName;
do {
dataName = Utils.getOption('T', args);
if (dataName.length() != 0) {
File dataset = new File(dataName);
exp.getDatasets().addElement(dataset);
}
} while (dataName.length() != 0);
}
System.err.println("Experiment:\n" + exp.toString());
if (saveFile.length() != 0)
write(saveFile, exp);
if (runExp) {
System.err.println("Initializing...");
exp.initialize();
System.err.println("Iterating...");
exp.runExperiment(verbose);
System.err.println("Postprocessing...");
exp.postProcess();
}
} catch (Exception ex) {
System.err.println(ex.getMessage());
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 6385 $");
}
} // Experiment